DEA-C01 Question 387
Select 2You are designing a data pipeline for a real-time analytics application. The incoming data is ingested from a streaming source and needs to be processed with low latency. The processed data must then be stored in a highly available and durable storage service for further querying. Which combination of AWS services would best meet these requirements?
- A
Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, and Amazon S3 for storage
- B
Amazon SQS for ingestion, AWS Lambda for processing, and Amazon RDS for storage
- C
Amazon Kinesis Data Firehose for ingestion and processing, and Amazon Redshift for storage
- D
Amazon MSK (Managed Streaming for Apache Kafka) for ingestion, AWS Glue for processing, and Amazon DynamoDB for storage
- E
Amazon Kinesis Data Streams for ingestion, Amazon EMR for processing, and Amazon S3 for storage
Show answer and explanation
Correct answers: A, E
Explanation
For real-time analytics with low latency, Amazon Kinesis Data Streams is an ideal choice for streaming data ingestion. AWS Lambda and Amazon EMR provide options for low-latency processing based on specific use cases, while Amazon S3 serves as a highly available and cost-effective storage solution for processed data. These combinations ensure the pipeline meets the requirements for real-time analytics applications.
- A. Correct.
Amazon Kinesis Data Streams is designed for real-time data ingestion, AWS Lambda allows for serverless, low-latency processing, and Amazon S3 is a durable and scalable storage solution suitable for analytics.
- B. Incorrect.
Amazon SQS is a message queuing service and not ideal for streaming data ingestion. While AWS Lambda is suitable for processing, Amazon RDS is not optimized for high-scale, analytical workloads.
- C. Incorrect.
While Amazon Kinesis Data Firehose supports data ingestion and basic transformations, it is not well-suited for real-time, low-latency processing. Amazon Redshift is excellent for analytics but may not meet the durability and high availability required for raw data storage.
- D. Incorrect.
Amazon MSK is a good choice for ingestion, but AWS Glue is not designed for ultra-low-latency real-time processing. DynamoDB is not an ideal storage solution for long-term analytics data.
- E. Correct.
Amazon Kinesis Data Streams supports real-time ingestion, Amazon EMR is highly scalable for low-latency data processing, and Amazon S3 offers durable and cost-effective storage for analytics.